Network control theory provides a potentially powerful approach for modeling direct electrical stimulation in humans [1]}. Building on recent advances in physics and engineering, network control theory characterizes a complex system as composed of nodes interconnected by edges [2]}, and then specifies a model of network dynamics to determine how external input affects the nodes' time-varying activity [3]}. Drawing on canonical results from linear systems and structural controllability [4]}, this approach was originally developed in the context of technological, mechanical, and other man-made systems [5]}, but has notable relevance for the study of natural processes from cell signaling [6]} to gene regulation [7]}. In applying such a theory to the human brain, one first represents the brain as a network of nodes (brain regions) interconnected by structural edges (white matter tracts) [8]}, and then one posits a model of system dynamics that specifies how control input affects neural dynamics via propagation along the tracts [9]}. Formal approaches built on this model address questions of where control points are positioned in the system [9]}, [11]}, [12]}, [13]}, as well as how to define spatiotemporal patterns of control input to move the system along a trajectory from an initial state to a desired final state [14]}, [15]}. Intuitively, these approaches may be particularly useful in probing the effects of stimulation [12]} and pharmacogenetic activation or inactivation [17]} for the purposes of guiding transitions between cognitive states or treating abnormalities of brain network dynamics such as epilepsy [18]}, [19]}, [20]}, psychosis [21]}, or bipolar disorder [22]}. However, this intuition has not yet been validated with direct electrical stimulation data.
